Energy-intensive manufacturing processes, such as induction melting, suffer from limited visibility into operational variability, hindering efforts to optimize energy use and productivity. Existing studies primarily focus on structural improvements or single-variable analysis, offering little support for dynamic, data-driven decision-making. This paper addresses this critical gap by proposing a novel framework that integrates full-cycle segmentation, unsupervised clustering, and multi-criteria decision-making (MCDM) to systematically discover, evaluate, and benchmark operational patterns in industrial melting. Unlike prior work that isolates temperature or energy profiles, the proposed method fuses high-frequency power, weight, and temperature signals to identify melting cycles using correlation-informed segmentation. K-means clustering is applied to full-process feature vectors, enabling interpretable groupings based on melting rate and energy-specific consumption. These clusters are then evaluated using an ensemble of MCDM techniques (SAW, TOPSIS, VIKOR) across both performance and operational dimensions. The framework is validated on a 16-month dataset from a Danish foundry, comprising over 3,400 melting cycles. Results reveal five distinct operational patterns and show that standardizing operations toward the top-performing cluster can reduce energy consumption by 9.3% and process time by 29%. Scientifically, this work contributes a unified pipeline for multi-sensor industrial process analytics, combining segmentation, unsupervised learning, and decision-support under a real-world context. Practically, it offers a scalable tool for identifying energy-efficient best practices and supports data-driven operational optimization in smart manufacturing environments.